基于语言和约束匹配器融合的元素匹配研究

在模式匹配方面已经出现了许多使用于特定应用领域的部分自动匹配方法,这种匹配方法结合了多种匹配技术以便能够在大规模的多样匹配环境中得到高的匹配率。提出了一种基于模式的元素匹配方法,它融合了语言和约束匹配器,使用了复合元素名称匹配器和神经网络匹配器,结合基于语言的匹配算法和最大优先策略的原则,以多重标准条件下复合名称匹配器的结果作为约束对模式元素进行归类。通过组合使用复合名称匹配器和神经网络匹配器,使得本方法可以应用于更复杂的匹配环境。

Element matching by concatenating linguistic-based matchers and constraint-based matcher

Although a lot of previous work on schema matching has developed many partial automatic matches for specific application domains,combining multiple match techniques enables achieving high accuracy for a large variety of match circumstances. In this context,we present a schema-based element matching approach that concatenates linguistic-based matchers and a constraint-based matcher.We propose a basic processing of our element level match approach in terms of a sequence of linguistic-based match and constraint-based match.We also provide a composite element name matcher to automatically combine linguistic-based match algorithms with a maximum priority strategy,and a neural network matcher to categorize elements of schemas by using element constraints with results from composite name matcher for joint consideration of multiple criteria.The concatenation of composite name matcher and neural network matcher enable our approach to adapt to more complex matching circumstance.